datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MetaMathQAView the project page:
https://meta-math.github.io/
see our paper at https://arxiv.org/abs/2309.12284
Note
All MetaMathQA data are augmented from the training sets of GSM8K and MATH.
None of the augmented data is from the testing set.
You can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set.
Model Details
MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model. It is… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/MetaMathQA.MetaMathQA-40Karxiv.org/abs/2309.12284
View the project page:
https://meta-math.github.io/
GSM8K_zh
Dataset
GSM8K_zh is a dataset for mathematical reasoning in Chinese, question-answer pairs are translated from GSM8K (https://github.com/openai/grade-school-math/tree/master) by GPT-3.5-Turbo with few-shot prompting.
The dataset consists of 7473 training samples and 1319 testing samples. The former is for supervised fine-tuning, while the latter is for evaluation.
for training samples, question_zh and answer_zh are question and answer keys, respectively;
for testing samples, only… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/GSM8K_zh.MetaMathQA_GSM8K_zh
Dataset
MetaMathQA_GSM8K_zh is a dataset for mathematical reasoning in Chinese,
question-answer pairs are translated from MetaMathQA (https://huggingface.co/datasets/meta-math/MetaMathQA) by GPT-3.5-Turbo with few-shot prompting.
The dataset consists of 231685 samples.
Citation
If you find the GSM8K_zh dataset useful for your projects/papers, please cite the following paper.
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/MetaMathQA_GSM8K_zh.MetaMathFewshot
A few-shot version of the MetaMath (https://huggingface.co/datasets/meta-math/MetaMathQA) dataset.
Each entry is formatted with 'question' and 'answer' keys. The 'question' key has a random number of query-answer pairs between 0 and 4 inclusive, before a final target query; the expected answer to this is stored in the content of 'answer'.
metamath-qwen2-math
Dataset Summary
Approximately 900k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from metamath-qa https://huggingface.co/datasets/meta-math/MetaMathQA and https://huggingface.co/datasets/AI-MO/NuminaMath-CoT with only none-synthetic dataset only. We only use the prompts from metamath-qa and get response with Qwen2-math-72-instruct and rejection-sampling, the solution is filted based on the official evaluation… See the full description on the dataset page: https://huggingface.co/datasets/yingyingzhang/metamath-qwen2-math.GSM8K_Backwardarxiv.org/abs/2309.12284
View the project page:
https://meta-math.github.io/
metamath_gsm8k
🚀 GSM8K training set
The original answer is "\n#### Value" and now is "\n#### Value\nThe answer is: Value", and the answer is cleaned, which is consistent with the answer format with "meta-math/MetaMathQA".
💻 Dataset Usage
Run the following command to load the data:
from datasets import load_dataset
dataset = load_dataset("shuyuej/metamath_gsm8k")
dataset = dataset['train']
print(dataset)
📝 Dataset modification codes
# coding=utf-8
import re
import… See the full description on the dataset page: https://huggingface.co/datasets/shuyuej/metamath_gsm8k.MetaMathQAMetaMathQAView the project page:
https://meta-math.github.io/
see our paper at https://arxiv.org/abs/2309.12284
Note
All MetaMathQA data are augmented from the training sets of GSM8K and MATH.
None of the augmented data is from the testing set.
You can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set.
Model Details
MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model. It is… See the full description on the dataset page: https://huggingface.co/datasets/agicorp/MetaMathQA.Vietnamese-395k-meta-math-MetaMathQA-gg-translatedlm-eval-results-ntnhan-Llama3-8B-MetaMath-private
Dataset Card for Evaluation run of ntnhan/Llama3-8B-MetaMath
Dataset automatically created during the evaluation run of model ntnhan/Llama3-8B-MetaMath
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-ntnhan-Llama3-8B-MetaMath-private.MetaMathQA-10K-TR
MetaMathQA-10K-TR (Turkish Mathematical Reasoning & CoT Dataset)
MetaMathQA-10K-TR, meta-math/MetaMathQA-40K veri setinden türetilmiş, Türkçe dilinde adım adım akıl yürütme (Chain-of-Thought - CoT) ve matematiksel problem çözme yeteneği kazandırmak amacıyla hazırlanmış 10.000 satırlık yüksek kaliteli bir veri setidir.
Bu veri seti, yerel Qwen 3.8 27B modeli kullanılarak özel olarak tasarlanmış prompt mühendisliği ve sıkı biçimlendirme kuralları ile Türkçe'ye çevrilmiş ve… See the full description on the dataset page: https://huggingface.co/datasets/WrittenWithRust/MetaMathQA-10K-TR.worked-examples-metamath-v0
Full MetaMathQA worked-examples pack
Source: meta-math/MetaMathQA (all 395k, all types).
Train: 355,688 instances (90% of families)
Holdout: 39,155 (eval/holdout_bare.jsonl)
Docs: arms/<arm>/docs.jsonl.gz (gunzip to use)
Tokens: tokenized/<arm>/shard-00000.npy (dolma2, EOS 100257)
Arm stats
{
"fade_shuffled": {
"n_docs": 1873620,
"n_tokens": 453279629
}
}
Metamath2Py
Links
Github with source code: https://github.com/kamushekp/metamath2py
Paper: https://github.com/kamushekp/metamath2py/blob/main/out/main.pdf
Dataset Structure
The Metamath2Py Dataset consists of the following components:
1. JSONL File on Hugging Face
The dataset is provided as a JSONL file, where each line is a JSON object with the following fields:
original_name: The original name of the statement in the Metamath system.
name: The statement name in our… See the full description on the dataset page: https://huggingface.co/datasets/kamushekp/Metamath2Py.MetaMathQA-40K-PTBRTradução do MetaMathQA-4k para portugues com NLLB 3.3b.
MetaMathQA-ShareGPT
MetaMathQA-ShareGPT
This repository contains the ShareGPT format version of the MetaMathQA dataset.
Dataset Description
The MetaMathQA-ShareGPT dataset is a transformed version of the MetaMathQA dataset, which has been reformatted to fit the ShareGPT conversation format. Each entry in the dataset consists of a series of user-assistant interactions, making it suitable for training and evaluating conversational models.
Format
Each entry in the dataset is… See the full description on the dataset page: https://huggingface.co/datasets/pbatra/MetaMathQA-ShareGPT.Vietnamese-meta-math-MetaMathQA-40K-gg-translatedMetaMathQA-300k300,000 samples from the meta-math/MetaMathQA dataset
MetaMathQA-Turkish-correctedmetamathqahttps://huggingface.co/datasets/meta-math/MetaMathQA
features: mathematics, single-turn, task
length: 395k
metamath_ja_950_reka3flash
metamath_ja_950_reka3flash
meta-math/MetaMathQAの最初の1000件をRekaAI/reka-flash-3で翻訳した後、フォーマットが維持されなかったものを除去しました。
データセットは1000件ありますが、実際は950件程度です。
また、フォーマットのみでクリーニングしたので、出力自体がおかしいものは除去できていません。このあたりは各自でクリーニングし直してください。
例
input
\nグレイシーとジョーは複素平面上で数を選んでいます。ジョーは点 $1+2i$ を選び、グレイシーは $-1+i$ を選びました。二人の点間の距離はどれくらいですか？\n\n
output
\n複素平面上の点 $(x_1,y_1)$ と $(x_2,y_2)$ の距離は、式 $\\sqrt{(x_2-x_1)^2+(y_2-y_1)^2}$ で求められます。 \nこの場合、ジョーの点は$(1,2)$、グレイシーの点は$(-1,1)$です。 \nしたがって、彼らの点間の距離は… See the full description on the dataset page: https://huggingface.co/datasets/kurogane/metamath_ja_950_reka3flash.MetaMathQA
MetaMath Dataset with "{"question": question, "answer": answer}" pairs.
💻 Dataset Usage
Run the following command to load the data:
from datasets import load_dataset
dataset = load_dataset("shuyuej/MetaMathQA")
dataset = dataset['train']
print(dataset)
📝 Dataset modification codes
# coding=utf-8
import jsonlines
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("meta-math/MetaMathQA")
dataset = dataset["train"]
data = []
for… See the full description on the dataset page: https://huggingface.co/datasets/shuyuej/MetaMathQA.metamath-ans-auglm-eval-results-meta-math-MetaMath-Mistral-7B-private
Dataset Card for Evaluation run of meta-math/MetaMath-Mistral-7B
Dataset automatically created during the evaluation run of model meta-math/MetaMath-Mistral-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-meta-math-MetaMath-Mistral-7B-private.cleand_meta-math_MetaMathQA元データ: https://huggingface.co/datasets/meta-math/MetaMathQA
データ件数: 394,369
平均トークン数: 233
最大トークン数: 2,874
合計トークン数: 91,798,611
ファイル形式: JSONL
ファイルサイズ: 297.9 MB
=================== 以下、加工内容をclaudeでまとめ。
MetaMathQAデータセット加工内容
データ読み込み・準備
HuggingFace Datasetsからmeta-math/MetaMathQAの訓練データ（395,000件）を読み込み
DeepSeek-R1-Distill-Qwen-32Bトークナイザーを使用してトークン数を計算
データ構造の理解・分析
全てのresponseが"The answer is:"で終わる統一フォーマットであることを確認
original_questionとresponseを結合してトークン数計算用テキストを作成… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_meta-math_MetaMathQA.TencentARC__MetaMath-Mistral-Pro-details
Dataset Card for Evaluation run of TencentARC/MetaMath-Mistral-Pro
Dataset automatically created during the evaluation run of model TencentARC/MetaMath-Mistral-Pro
The dataset is composed of 44 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/TencentARC__MetaMath-Mistral-Pro-details.ro-MetaMathQAThis dataset is a translation of meta-math/MetaMathQA, using LLMic, a bilingual Romanian-English LLM.
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
journal={arXiv preprint arXiv:2309.12284},
year={2023}
}
@article{buadoiu2025llmic,
title={LLMic:… See the full description on the dataset page: https://huggingface.co/datasets/faur-ai/ro-MetaMathQA.MetaMathQA-40K-GPT3.5MetaMathQA-40K adapted to the GPT3.5 dataset format in JSONL for Fine-tuning. Following the following model:
{"messages": [{"role": "system", "content": ""}, {"role": "user", "content": ""}, {"role": "assistant", "content": ""}]}
mirror-meta-math__MetaMathQAView the project page:
https://meta-math.github.io/
see our paper at https://arxiv.org/abs/2309.12284
Note
All MetaMathQA data are augmented from the training sets of GSM8K and MATH.
None of the augmented data is from the testing set.
You can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set.
Model Details
MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model.… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-meta-math__MetaMathQA.